Detection of Data in Signals with Data Pattern Dependent Signal Distortion

ABSTRACT

A detection system and method may be used to detect data transmitted in a signal with data pattern dependent signal distortion. In general, a detection system and method compares samples of a received signal with stored samples of distorted signals associated with known data patterns and selects the known data patterns that correspond most closely with the samples of the received signal. The detection system and method may thus mitigate the effects of data pattern dependent signal distortion.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional PatentApplication Ser. No. 61/158,823, filed on Mar. 10, 2009, which is fullyincorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to data detection and more particularly,to detection of data in signals with data pattern dependent signaldistortion.

BACKGROUND INFORMATION

Signals may be used to transmit data over distances. In opticalcommunication systems, for example, data may be modulated on one or moreoptical wavelengths to produce modulated optical signals that may betransmitted over optical waveguides such as optical fibers. Onemodulation scheme that may be used in optical communication systems isphase shift keying in which data is transmitted by modulating the phaseof an optical wavelength such that the phase or phase transition of theoptical wavelength represents symbols encoding one or more bits. In abinary phase-shift keying (BPSK) modulation scheme, for example, twophases may be used to represent 1 bit per symbol. In a quadraturephase-shift keying (QPSK) modulation scheme, four phases may be used toencode 2 bits per symbol. Other phase shift keying formats includedifferential phase shift keying (DPSK) formats and variations of phaseshift keying and differential phase shift keying formats, such asreturn-to-zero DPSK (RZ-DPSK). Another modulation format is quadratureamplitude modulation (QAM) in which information is modulated onto bothphase and amplitude of a transmitted signal.

To receive the data, the signals may be detected and demodulated. Inphase modulated optical communication systems, for example, coherentoptical receivers may use coherent detection to detect modulated opticalsignals and may provide sensitivity advantages over receivers usingnon-coherent detection. Digital signal processing (DSP) may beimplemented in such systems for processing the received signals toprovide a demodulated data. Digital signal processing of the receivedsignal provides speed and flexibility and may be used to perform avariety of functions including estimation of the carrier phase of thereceived signals and data detection using the estimated carrier phase.

Distortion of a signal (e.g., during transmission), however, mayadversely affect the integrity of the data that is obtained afterdetecting and demodulating the signal. In optical communications systemsusing phase modulation schemes, nonlinear effects, such has self phasemodulation (SPM), may cause phase distortion in the modulated signal,which may significantly degrade coherent-detection performance anddiminish the receiver-sensitivity advantage that coherent detection hasover non-coherent detection. The degradation in BPSK signals isdescribed in greater detail in Yi Cai, et. al., “On Performance ofCoherent Phase-Shift-Keying Modulation in 40 Gb/s Long-Haul OpticalFiber Transmission Systems”, Optical Fiber Communication and theNational Fiber Optic Engineers Conference, 2006, paper JThB 11 (March2006), which is fully incorporated herein by reference.

The distortion in a modulated signal, such as phase distortion in amodulated optical signal, may often be dependent on the data pattern orbit-pattern. FIGS. 9 and 10 illustrate bit-pattern dependent phasedistortions that may occur in an optical communication system based on asingle-channel nonlinear propagation simulation. FIG. 9 shows aconstellation diagram of a distorted BPSK signal in which theconstellation points extend above and below the real axis, indicatingthe effect of phase distortion. FIG. 10 shows phase distortionscorresponding to various bit patterns and illustrates how the phasedistortions are dependent on bit pattern.

Methods have been proposed for mitigating the performance penaltyinduced by data-pattern dependent distortion such as nonlinear phasedistortion in optical coherent receivers. One method compensates thenonlinear phase distortion based on estimated phase distortion as afunction of received signal intensity, for example, as described in K.Ho and J. Kahn, “Electronic compensation technique to mitigate nonlinearphase noise,” Journal of Lightwave Technology, 22, 779-783 (2004) and inK. Kikuchi “Electronic Post-compensation for nonlinear PhaseFluctuations in a 1000-km 20-Gb/s Optical Quadrature Phase-shift KeyingTransmission System Using the Digital Coherent Receiver,” OpticsExpress, Vol. 16, No. 2, 2007, which are fully incorporated herein byreference. This method may fail, however, when optical signal intensitychanges significantly during propagation, which is often the case inoptical communication systems employing a practical chromatic dispersionmap.

Another method compensates nonlinear distortion by digitalbackpropagation, for example, as described in X. Li, X. Chen, G.Goldfarb, E. Mateo, I. Kim, F. Yaman and G. Li, “Electronicpost-compensation of WDM transmission impairments using coherentdetection and digital signal processing,” Optics Express, vol. 16, no.2, pp. 880-888, Jan. 21, 2008, and in E. Ip, A. P. T. Lau, D. J. Barrosand J. M. Kahn, “Compensation of chromatic dispersion and nonlinearityusing simplified digital backpropagation,” Proc. of OSA Topical Meetingon Coherent Optical Technologies and Applications, Boston, Mass., Jul.13-16, 2008, which are fully incorporated herein by reference. Thisbackpropagation method involves complicated calculations and may not bepractical in 10-100 Gb/s optical transmissions.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages will be better understood byreading the following detailed description, taken together with thedrawings wherein:

FIG. 1 is a block diagram of one exemplary embodiment of a systemconsistent with the present disclosure;

FIG. 2 is a block diagram of one exemplary embodiment of a receiverconsistent with the present disclosure.

FIG. 3 is a block diagram of a communication system including a receiverwith a data detection system for detecting data in a signal with datapattern dependent signal distortion, consistent with an embodiment ofthe present disclosure.

FIG. 4 is a flow chart illustrating a method for detecting data in asignal with data pattern dependent signal distortion, consistent with anembodiment of the present disclosure.

FIG. 5 is a flow chart illustrating a method for training a datadetection system, consistent with an embodiment of the presentdisclosure.

FIG. 6 is a plot illustrating a minimum Euclidean distance for variousbit patterns calculated using different maximum a posteriori probability(MAP) detection schemes, consistent with embodiments of the presentdisclosure.

FIG. 7 is a plot illustrating a Q factor as a function of channel powerfor a simulated optical system using different MAP detection schemes,consistent with embodiments described herein.

FIG. 8 is a plot illustrating a Q factor as a function of channel powerfor a simulated optical system using different MAP detection schemes,consistent with embodiments described herein.

FIG. 9 is a constellation diagram illustrating a distorted BPSK signal.

FIG. 10 is a diagram illustrating bit-pattern dependent phasedistortions associated with different bit patterns.

DETAILED DESCRIPTION

A detection system and method, consistent with the present disclosure,may be used to detect data transmitted in a signal with data patterndependent signal distortion. In general, the detection system and methodcompares samples of a received signal with stored samples of distortedsignals associated with known data patterns and selects the known datapatterns that correspond most closely with the samples of the receivedsignal. The detection system and method may thus mitigate the effects ofdata pattern dependent signal distortion.

According to exemplary embodiments, detection systems and methodsdescribed herein may be used in an optical communication system tomitigate the effects of bit-pattern dependent phase distortion in phasemodulated optical signals. The phase modulated optical signals may bemodulated using a phase shift keying modulation scheme such as BPSK,QPSK, DPSK, DQPSK, or some other higher order nPSK scheme, or somevariation thereof (e.g., RZ-DPSK). In an optical communication system,bit-pattern dependent signal distortion may be caused by fiber nonlineareffects such as self phase modulation (SPM) or other nonlinearities. Thedetection systems and methods described herein may also be used in othercommunication systems in which data pattern dependent signal distortionoccurs in a transmitted signal.

FIG. 1 is a simplified block diagram of one exemplary embodiment of aWDM transmission system 100 in which a detection system and method maybe used consistent with the present disclosure. The transmission systemserves to transmit a plurality of optical channels over an opticalinformation path 102 from a transmitting terminal 104 to one or moreremotely located receiving terminals 106. The exemplary system 100 maybe a long-haul submarine system configured for transmitting the channelsfrom a transmitter to a receiver at a distance of 5,000 km, or more.Although exemplary embodiments are described in the context of anoptical system and are useful in connection with a long-haul WDM opticalsystem, the broad concepts discussed herein may be implemented in othercommunication systems transmitting and receiving other types of signals.

Those skilled in the art will recognize that the system 100 has beendepicted as a highly simplified point-to-point system for ease ofexplanation. For example, the transmitting terminal 104 and receivingterminal 106 may, of course, both be configured as transceivers, wherebyeach may be configured to perform both transmitting and receivingfunctions. For ease of explanation, however, the terminals are depictedand described herein with respect to only a transmitting or receivingfunction. It is to be understood that a system and method consistentwith the disclosure may be incorporated into a wide variety of networkcomponents and configurations. The illustrated exemplary embodimentsherein are provided only by way of explanation, not of limitation.

In the illustrated exemplary embodiment, each of a plurality oftransmitters TX1, TX2 . . . TXN receives a data signal on an associatedinput port 108-1, 108-2 . . . 108-N, and transmits the data signal onassociated wavelength λ₁, λ₂ . . . λ_(N). One or more of thetransmitters TX1, TX2 . . . TXN may be configured to modulate data onthe associated wavelength with using a PSK modulation format, such asDBPSK, DQPSK, RZ-DPSK, RZ-DQPSK, etc. The transmitters, of course, areshown in highly simplified form for ease of explanation. Those skilledin the art will recognize that each transmitter may include electricaland optical components configured for transmitting the data signal atits associated wavelength with a desired amplitude and modulation.

The transmitted wavelengths or channels are respectively carried on aplurality of paths 110-1, 110-2 . . . 110-N. The data channels arecombined into an aggregate signal on optical path 102 by a multiplexeror combiner 112. The optical information path 102 may include opticalfiber waveguides, optical amplifiers, optical filters, dispersioncompensating modules, and other active and passive components.

The aggregate signal may be received at one or more remote receivingterminals 106. A demultiplexer 114 separates the transmitted channels atwavelengths λ₁, λ₂ . . . λ_(N) onto associated paths 116-1, 116-2 . . .116-N coupled to associated receivers RX1, RX2 . . . RXN. One or more ofthe receivers RX1, RX2 . . . RXN may be configured to demodulate thetransmitted signal and provide an associated output data signal on anassociated output path 118-1, 118-2, 118-3, 118-N.

FIG. 2 is a simplified block diagram of one exemplary receiver 200consistent with the present disclosure. The illustrated exemplaryembodiment 200 includes a coherent receiver configuration 202 forreceiving an input signal on path 116-N and a digital signal processing(DSP) circuit 204 for processing the output of the coherent receiver toprovide an output data signal on path 118-N. Data is modulated on thecarrier wavelength λ_(N) of the optical input signal according to a PSKmodulation format. The coherent receiver 202 converts the receivedoptical input signal into one or more digital signals that are providedas inputs to the DSP circuit 204. The DSP circuit demodulates the datafrom the digital signals to provide an output data stream on path 118-Nrepresentative of the data modulated on the carrier wavelength λ_(N).

The coherent receiver 202 may take a variety of configurations. In theillustrated exemplary embodiment, the receiver includes a polarizationbeam splitter (PBS) 206, first and second 90° optical hybrids 208, 210,a local oscillator (LO) 212, balanced detectors 214, 216, 218, 220 andanalog-to-digital (A/D) converters 222, 224, 226, 228. The operations ofthese components in a coherent optical signal receiver are brieflydescribed below. In general, different polarizations of the inputoptical signal are split onto separate paths by the PBS 206. Eachpolarization is provided to an associated 90° optical hybrid 208, 210.Each optical hybrid mixes its input signal with the four quadrilateralstates of the LO oscillator signal in the complex-field space. Eachoptical hybrid then delivers the four mixed signals to two pairs ofbalanced detectors 214, 216, 218, 220. The outputs of the balanceddetectors are converted to digital signals by the A/D converters 222,224, 226, 228.

The digital outputs of the A/D converters are provided as inputs to theDSP circuit 204. In general, DSP involves processing of signals usingone or more application specific integrated circuits (ASICS) and/orspecial purpose processors configured for performing specificinstruction sequences, e.g. directly and/or under the control ofsoftware instructions. In the illustrated exemplary embodiment, the DSPcircuit 204 is shown as including a pre-processing function 230, anoptional local oscillator (LO) frequency offset tracking function 232, acarrier phase estimation function 234, a bit decision function 236 andan optional PRBS bit error rate function 238. These functions may beimplemented in a variety of configurations using any combination ofhardware, software and/or firmware. Although the functions areillustrated separately, it is to be understood that any one or more ofthe functions may be performed in a single integrated circuit orprocessor, or in a combination of integrated circuits and/or processors.Also, the integrated circuits and/or processors implementing the DSPfunctions may be shared among the illustrated functions in whole or inpart.

The pre-processing function 230 of the DSP may include various opticalsignal detection functions implemented in different types of DSP-basedcoherent detection receivers. The pre-processing functions may include,for example, a waveform recovery and alignment function, a deterministicdistortion compensation function, a clock recovery function, asynchronized data re-sampling function, and a polarization tracking andpolarization mode dispersion (PMD) compensation function. The optionalLO frequency offset tracking function 232 may be configured to track andcompensate for frequency offset between the received signal and the LOsignal.

In general, since the data in a PSK modulated signal is encoded in thephase of an optical carrier signal, demodulation of a PSK modulatedsignal in a DSP-based receiver involves estimating and tracking thecarrier phase. The carrier phase estimation function 234 is provided forthis purpose and may be configured as a dual stage carrier phaseestimation function. The carrier phase estimate from the carrier phaseestimation function is provided to a bit decision function 236 whichdetermines the data or bit values represented by the carrier phase inthe modulated signal and mitigates the effects of data pattern dependentsignal distortion such as phase distortion. The data may then beprovided on an output on path 118-N representative of the data modulatedon the carrier wavelength λ_(N). The optional error rate testingfunction 238 may be configured for performing a bit error rate (BER)test on training sequence signal for testing performance of the DSPcircuit 204.

FIG. 3 shows a simplified communication system incorporating a detectionsystem 300 that may be used to perform the bit decision function 236(FIG. 2) and that mitigates the effects of data pattern dependentdistortion in transmitted signals. The detection system 300 may beimplemented in the DSP circuit 204 coupled to the coherent receiver 202as described above. For simplicity and ease of explanation, the systemis shown including only a single coherent receiver for receiving only asingle wavelength. It is to be understood that the system may beconfigured as a WDM system including demultiplexer and a plurality ofreceivers for receiving multiple wavelengths. In other embodiments, thedetection system 300 may be used in other communication systems withother types of receivers.

In the exemplary embodiment, the coherent receiver 202 receives, detectsand digitizes a transmitted signal transmitted by a transmitter ortransmitting terminal 104. In an optical system transmitting phasemodulated optical signals, for example, the coherent receiver 202receives the optical signal, detects the electrical field of thereceived optical signal, and produces digitized samples representing thephase of the symbols in the optical signal and thus the data modulatedon the optical signal. The detection system 300 may then process samplesof the received signal to determine the data values (e.g., the bitvalues) represented by the samples and provides an output including thedata or bit values. In the exemplary embodiment, the detection system300 processes the samples by comparing the samples to stored samplesassociated with known data patterns and selecting the known datapatterns that correspond most closely to the received samples.

The detection system 300 includes a distorted signal table 310 forstoring distorted signal samples associated with known data patterns anda detector 320 for comparing received signal samples with stored signalsamples and selecting known data patterns that correspond most closely.The detection system 300 may also include a shift register 330 to obtainreceived signal samples within a shifting data pattern window having alength corresponding to the length of the known data patterns stored inthe distorted signal table 310. The detector 320 may then compare thereceived samples within the shifting data pattern window to the storedsamples in the distorted signal table 310. The distorted signal table310 may be stored, for example, in a memory within or coupled to the DSPcircuit. The detector 320 and shift register 330 may be implemented ashardware, software, firmware, or a combination thereof in the DSPcircuit.

In the exemplary embodiment, the data patterns are N-bit patternsincluding a pattern of a predetermined number (N) of bits (e.g., a 5-bitpattern may include 00000, 00001, 00010, . . . ). As such, the distortedsignal table 310 may be an N-bit distorted signal table that stores bitpatterns (and associated signal samples) having a length of N bits andthe shift register 330 may be an N-bit shift register that provides anN-bit shifting window that obtains received signal samples within thewindow. An example of 5-bit patterns and associated signal samples froman exemplary optical BPSK modulated signal is provided in Table 1 below.

TABLE 1 Bit Pattern Samples 00000   0.3 − 0.1i   0.3 − 0.2i   0.3 − 0.1i  0.3 − 0.1i   0.4 − 0.1i 00001   0.3 − 0.0i   0.3 − 0.0i   0.4 + 0.0i  0.4 − 0.0i −0.4 + 0.1i 00010   0.3 − 0.0i   0.4 − 0.1i   0.4 − 0.1i−0.4 + 0.1i   0.4 − 0.1i . . . . . . 11110 −0.4 − 0.1i −0.3 − 0.2i −0.3− 0.1i −0.4 − 0.1i   0.3 + 0.2i 11111 −0.4 − 0.2i −0.4 − 0.1i −0.3 −0.1i −0.4 − 0.0i −0.4 − 0.1i

When the received signal samples within the shifting data pattern windoware fed to the detector 320, the detector 320 selects known datapatterns that correspond most closely by using a maximum a posterioriprobability (MAP) detection algorithm. For example, the detector 320 maycalculate and compare the Euclidean distances between the receivedsamples within the shifting window and the samples in the distortedsignal table. The known data pattern in the distorted signal table 310with the minimum Euclidean distance to the received samples is selectedas the MAP decision. The Euclidean distance between received sampleswithin an N-bit window (rs₁, rs₂, . . . , rs_(N)) and stored samples inan N-bit distorted signal table (ss₁, ss₂, . . . , ss_(N)) may becalculated as follows:

ED=(rs₁−ss₁)²+(rs₂−ss₂)²+ . . . +(rs_(N)−ss_(N))²

Other similar algorithms may also be used to select known data patternsthat correspond most closely. According to another embodiment, forexample, a maximum correlation criterion may be used to select knowndata patterns that most closely correspond. A Chase algorithm may alsobe used to increase the speed of a minimum Euclidean distance or maximumcorrelation search of the distorted signal table. According to a furtherembodiment, a maximum likelihood sequence estimation (MLSE) algorithmmay be used to select known data patterns that correspond most closely.

The detection system 300 may further include a trainer 340 for trainingthe system with distorted signals representing known bit patterns andfor generating the distorted signal table 310. The trainer 340 may beimplemented as hardware, software, firmware, or a combination thereof inthe DSP circuit. To perform a training function, a preset trainingsequence, such as a pseudo random bit sequence (PRBS), may betransmitted by the transmitter 104. The coherent receiver 202 receives,detects and digitizes the training sequence signal, which may bedistorted as a result of data pattern dependent distortion (e.g., phasedistortion in an optical signal).

The shift register 330 obtains the received training sequence signalsamples within the shifting data pattern window and feeds the samples tothe trainer 340. The trainer 340 arranges the received signal samplesinto data pattern dependent sets based on the data pattern in thewindow. For N-bit data patterns, for example, the trainer 340 arrangesthe received signal samples based on an N-bit pattern in an N-bit windowaround each bit. In one example, 5-bit data patterns may be arrangedsuch that signal samples associated with 00000 bit patterns are arrangedin a set, signal samples associated with 00001 bit patterns are arrangedin a set, signal samples associated with 00010 bit patterns are arrangedin a set, etc. The trainer 340 may then average the samples in each setto mitigate noise effects and store the averaged samples in memory asthe distorted signal table 310 indexed by the bit patterns.

The trainer 340 may perform the training and generate the distortedsignal table 310 at the initial stage of system operation. The trainer340 may also update the distorted signal table during system operationsusing preset non-user data. Updating the signal table allows the penaltymitigation to adapt to changes, such as polarization mode dispersion(PMD), in the transmission system.

In some embodiments, the detection system 300 may also use soft decisionforward error correction (FEC) to improve performance. FEC involvesinsertion of a suitable error correction code into a transmitted datastream to facilitate detection and correction of data errors about whichthere is no previously known information. Error correction codes aregenerated in an FEC encoder (e.g., in the transmitter 104) for the datastream. FEC encoding/decoding may be implemented according to a varietyof FEC schemes including, but not limited to, the linear and cyclicHamming codes, the cyclic Bose-Chaudhuri-Hocquenghem (BCH) codes, theconvolutional (Viterbi) codes, the cyclic Golay and Fire codes, and somenewer codes such as the Turbo convolutional and product codes (TCC, TPC)and the low density parity check (LDPC) code.

In soft decision FEC, multiple bit “soft” information is generated thatrepresents a confidence level or reliability of the received data (e.g.,whether a bit is very likely one, likely one, likely zero, or mostlikely zero). The additional “soft” information enables more efficientFEC decoding. Examples of soft decision FEC are disclosed in greaterdetail in U.S. Pat. No. 7,398,454, U.S. Patent Application PublicationNo. 2006/0136798, and U.S. patent application Ser. No. 12/108,155, allof which are fully incorporated herein by reference.

To implement soft decision FEC decoding, the detection system 300 mayinclude a soft decision FEC decoder 350 in combination with the detector320. The detector 320 may generate a soft-decision data stream, and thesoft decision FEC decoder 350 receives the soft-decision data stream,recovers the error correction codes and uses them to correct any errorsin the received data stream. In an embodiment, the detector 320 maycalculate a reliability of each decision bit to generate thesoft-decision data stream. The reliability calculation may be based onthe calculated Euclidean distances, maximum correlation criterion, orother criteria representing how closely the received signal samplescorrespond to the known bit patterns.

The detector 320 may also be responsive to feedback from the FEC decoder350 to adjust the soft information iteratively, which may furtherimprove the system performance. If the FEC decoder 350 corrects one ofthe bit values in a received N-bit pattern, for example, the softinformation fed back to the detector 320 for that bit pattern reflectsthe corrected bit. The corrected soft information may then be used bythe detector 320 to improve the selection of bit patterns thatcorrespond more closely, for example, by updating the distorted signaltable 310.

FIGS. 4 and 5 show methods consistent with the present disclosure. FIG.4 illustrates a detection method for detecting data in a signal withdata pattern dependent signal distortion. This detection method may beimplemented using the systems shown in FIGS. 1-3 or in other systemsthat receive and detect signals having data pattern dependent signaldistortion. According to the detection method, distorted signal samplesassociated with known data patterns are provided 410 (e.g., by trainingand creating an N-bit distorted signal table). The detection method alsoincludes receiving 412 digitized signal samples (e.g., from a coherentreceiver) and obtaining 414 samples within a sliding data pattern window(e.g., provided by an N-bit shift register). The detection methodfurther includes comparing 416 samples within the sliding data patternwindow with the samples associated with the known data patterns andselecting 418 the data patterns that correspond most closely with thesamples in the data pattern window (e.g., using MAP detectiontechniques).

FIG. 5 illustrates a training method for training a system for detectingdata in a signal with data pattern dependent signal distortion. Thistraining method may be implemented using the systems shown in FIGS. 1-3or in other systems that receive and detect signals having data patterndependent signal distortion. The training method includes receiving 510a signal representing a training sequence (e.g., a PRBS) including datapattern dependent distortion and digitizing 512 the received signal toproduce signal samples associated with the training data sequence (e.g.,using a coherent receiver). The training method also includes arranging514 the received signal samples into data pattern dependent sets andstoring 516 the received signal samples as a distorted signal tableindexed by data patterns.

FIGS. 6-8 illustrate the effectiveness of the system and method formitigating data pattern dependent distortion in transmitted signals in asimulated system. The simulated system was based on a 9000 km 40 Gb/sWDM coherent RZ-BPSK system. FIG. 6 illustrates the minimum Euclideandistances of each N-bit pattern to other N-bit patterns using differentN-bit windows (e.g., a 1-bit hard decision, a 7-bit MAP, a 4-bit MAP,and a 3-bit MAP) as compared to the ideal and a back-to-back (B2B)noise-loading simulation. As indicated, the 7-bit, 5-bit and 3-bit MAPdetection schemes all have minimum Euclidean distances between bitpatterns that are closer to the ideal and thus have higher Q factors andlarger as compared to the 1-bit hard decision detection.

FIGS. 7 and 8 illustrate the Q factors as a function of channel power.As shown in FIG. 7, the Q factor as a function of channel power ishigher for a 7-bit MAP detection as compared to a 5-bit and 3-bit MAPdetection and 1-bit hard decision detection. FIG. 7 also shows that the7-bit MAP detection may significantly improve performance when there arehigher nonlinearities. FIG. 8 further illustrates the Q factors as afunction of channel power for a 5-bit MAP detection using 2 samples persymbol as compared to a 5-bit MAP detection using 1 sample per symbol.As indicated in the simulated system, using 2 samples per symbol mayonly help when the system is highly nonlinear and may degrade MAPdetection performance in linear and quasi-linear regimes.

Accordingly, embodiments of the detection system and method describedherein may mitigate the data pattern dependent signal distortion andimprove performance of a communication system such as an opticalcommunication system.

Consistent with one embodiment, a system is provided for detecting datain a signal with data pattern dependent signal distortion. The systemincludes a distorted signal table configured to store a plurality ofknown data patterns and samples of distorted signals associated with theknown data patterns. The system also includes a data shift registerconfigured to obtain samples of a received signal within a shifting datapattern window having a length corresponding to a length of the knowndata patterns. The system further includes a detector configured tocompare the samples in the data pattern window with the samples in thedistorted signal table and to select the known data patterns in thedistorted signal table that correspond most closely with the samples inthe data pattern window.

Consistent with another embodiment, digital signal processor (DSP) basedreceiver includes: a coherent receiver configured to receive, detect anddigitize a modulated optical signal to produce received signal samples;and a DSP configured to store a distorted signal table including aplurality of known data patterns and samples of distorted signalsassociated with the known data patterns, to obtain samples of a receivedsignal within a shifting data pattern window having a lengthcorresponding to a length of the known data patterns, to compare thesamples in the data pattern window with the samples in the distortedsignal table, and to select the known data patterns in the distortedsignal table that correspond most closely with the samples in the datapattern window.

Consistent with a further embodiment, a detection method is provided fordetecting data in a signal with data pattern dependent signaldistortion. The detection method includes: providing a distorted signaltable including a plurality of known data patterns and samples ofdistorted signals associated with the known data patterns; receiving adigitized signal including a plurality of digitized samples; andprocessing the digitized signal in a digital signal processor to obtainsamples in the received signal within a sliding data pattern windowhaving a length corresponding to a length of the known data patterns, tocompare the samples in the data pattern window with the samplesassociated with the known signal patterns, and to select the datapatterns in the distorted signal table that correspond most closely withthe samples in the data pattern window.

Consistent with yet another embodiment, a training method is providedfor training a system for detecting data in a signal with data patterndependent signal distortion. The training method includes: receiving ina receiver a signal representing a training data sequence; digitizingthe received signal to produce signal samples associated with thetraining data sequence; and processing the digitized received signal ina digital signal processor to arrange the received signal samples intodata pattern dependent sets based on data patterns and to store thereceived signal samples as a distorted signal table indexed by the datapatterns.

While the principles of the invention have been described herein, it isto be understood by those skilled in the art that this description ismade only by way of example and not as a limitation as to the scope ofthe invention. Other embodiments are contemplated within the scope ofthe present invention in addition to the exemplary embodiments shown anddescribed herein. Modifications and substitutions by one of ordinaryskill in the art are considered to be within the scope of the presentinvention, which is not to be limited except by the following claims.

1. A system for detecting data in a signal with data pattern dependent signal distortion, the system comprising: a distorted signal table configured to store a plurality of known data patterns and samples of distorted signals associated with the known data patterns; a data shift register configured to obtain samples of a received signal within a shifting data pattern window having a length corresponding to a length of the known data patterns; and a detector configured to compare the samples in the data pattern window with the samples in the distorted signal table and to select the known data patterns in the distorted signal table that correspond most closely with the samples in the data pattern window.
 2. The system of claim 1 further comprising a trainer configured to generate the samples associated with the known data patterns stored as the distorted signal table.
 3. The system of claim 2 wherein the trainer is configured to receive signal samples associated with a training data sequence, to arrange the received signal samples into data pattern dependent sets based on data patterns, and to store the received signal samples as the distorted signal table indexed by the data patterns.
 4. The system of claim 1 further comprising a soft decision forward error correction (FEC) decoder configured to receive a soft-decision data stream from the detector and to decode encoded data represented by the soft decision data stream to produce decoded data.
 5. The system of claim 4 wherein the soft decision FEC decoder is configured to provide feedback to the detector, and the detector is configured to adjust the soft-decision data stream in response to the feedback.
 6. The system of claim 1 wherein the distorted signal table is an N-bit signal table configured to store N-bit data patterns having a predetermined number (N) of bits, and wherein the data pattern window is an N-bit window.
 7. The system of claim 1 wherein the detector is configured to provide maximum a posteriori probability (MAP) detection.
 8. The system of claim 1 wherein the detector is configured to compare the samples and select the known data patterns by calculating Euclidean distances between the samples in the data pattern window and the samples in the distorted signal table and by selecting the samples in the distorted signal table having the minimum Euclidean distances.
 9. The system of claim 1 wherein the received signal is an electrical signal converted from a modulated optical signal on which data is modulated using phase shift keying, and wherein the samples represent a phase of each symbol in the modulated optical signal.
 10. A digital signal processor (DSP) based receiver comprising: a coherent receiver configured to receive, detect and digitize a modulated optical signal to produce received signal samples; and a DSP configured to store a distorted signal table including a plurality of known data patterns and samples of distorted signals associated with the known data patterns, to obtain samples of a received signal within a shifting data pattern window having a length corresponding to a length of the known data patterns, to compare the samples in the data pattern window with the samples in the distorted signal table, and to select the known data patterns in the distorted signal table that correspond most closely with the samples in the data pattern window.
 11. The DSP based receiver of claim 10 wherein the modulated optical signal is modulated using phase shift keying, and wherein each of the samples represent a phase of the modulated optical signal.
 12. The DSP based receiver of claim 10 wherein the distorted signal table is an N-bit signal table configured to store N-bit data patterns having a predetermined number (N) of bits, and wherein the data pattern window is an N-bit window.
 13. The DSP based receiver of claim 10 wherein the DSP is configured to receive signal samples associated with a training data sequence, to arrange the received signal samples into data pattern dependent sets based on data patterns, and to store the received signal samples as the distorted signal table indexed by the data patterns.
 14. The DSP based receiver of claim 10 further comprising a soft decision forward error correction (FEC) decoder configured to receive a soft-decision data stream from the detector and to decode encoded data represented by the soft decision data stream to produce decoded data.
 15. A detection method for detecting data in a signal with data pattern dependent signal distortion, the method comprising: providing a distorted signal table including a plurality of known data patterns and samples of distorted signals associated with the known data patterns; receiving a digitized signal including a plurality of digitized samples; processing the digitized signal in a digital signal processor to obtain a segment of samples in the received signal having a length corresponding to a length of the known data patterns, to compare the samples in the segment of the received signal with the samples associated with the known signal patterns, and to select the data patterns in the distorted signal table that correspond most closely with the samples in the segment of the received signal.
 16. The detection method of claim 15 wherein the known data patterns are N-bit data patterns having a predetermined number (N) of bits.
 17. The detection method of claim 15 wherein comparing the samples includes calculating Euclidean distances between the samples in the segment of the received signal and the samples in the distorted signal table, and wherein selecting the data patterns includes selecting data patterns associated with the samples in the distorted signal table having the minimum Euclidean distances.
 18. The detection method of claim 15 further comprising: updating the known data patterns and samples of distorted signals associated with the known data patterns.
 19. A training method for training a system for detecting data in a signal with data pattern dependent signal distortion, the method comprising: receiving in a receiver a signal representing a training data sequence; digitizing the received signal to produce signal samples associated with the training data sequence; and processing the digitized received signal in a digital signal processor to arrange the received signal samples into data pattern dependent sets based on data patterns and to store the received signal samples as a distorted signal table indexed by the data patterns.
 20. The training method of claim 19 further comprising averaging the samples in each of the data pattern dependent sets.
 21. The system of claim 1 wherein the transmitted signal transmits data at about 100 Gb/s. 